Measuring Atmospheric Dry Deposition to Urban Surfaces

Surrogate surfaces are used to measure atmospheric dry deposition of contaminants, and are sometimes designed intentionally with simple geometry to estimate the lower limit of the flux to any surface. However, most surrogate surfaces have a small collection area: long periods of dry weather may be needed to obtain sufficient deposited contaminants to be detected and quantified, and such exposure periods may not be common in wet climates. In this study, two relatively large surrogate surfaces—disks with surface areas \u3e 1 m2—were designed to measure dry deposition of F-, Cl-, SO42-, and NO3- in Syracuse, NY. Results indicate that good reproducibility is possible for measurements with exposure periods of 2-6 days. Computational Fluid Dynamics modeling shows that the boundary layer thickness varies somewhat over the disk, but average fluxes to different sections of the disk differ by only 8%. This study also proposes a new method to measure dry deposition to urban surfaces by measuring the removal of dry deposited material in runoff samples collected from an urban surface during a rainstorm at various time steps. For this method to work, the amount of dry deposited mass must be substantially greater than the amount contributed by precipitation. As an example calculation, the amount of SO42- deposited to the roof of the War Memorial Arena in downtown Syracuse, NY is estimated using dry deposition data from the disks and compared to the amount of SO42- in the precipitation for a hypothetical storm. The results show that it may be possible to measure the removal of SO42- and other contaminants from the roof by stormwater runoff during a subsequent rainstorm

The Geometric Maximum Traveling Salesman Problem

We consider the traveling salesman problem when the cities are points in R^d for some fixed d and distances are computed according to geometric distances, determined by some norm. We show that for any polyhedral norm, the problem of finding a tour of maximum length can be solved in polynomial time. If arithmetic operations are assumed to take unit time, our algorithms run in time O(n^{f-2} log n), where f is the number of facets of the polyhedron determining the polyhedral norm. Thus for example we have O(n^2 log n) algorithms for the cases of points in the plane under the Rectilinear and Sup norms. This is in contrast to the fact that finding a minimum length tour in each case is NP-hard. Our approach can be extended to the more general case of quasi-norms with not necessarily symmetric unit ball, where we get a complexity of O(n^{2f-2} log n). For the special case of two-dimensional metrics with f=4 (which includes the Rectilinear and Sup norms), we present a simple algorithm with O(n) running time. The algorithm does not use any indirect addressing, so its running time remains valid even in comparison based models in which sorting requires Omega(n \log n) time. The basic mechanism of the algorithm provides some intuition on why polyhedral norms allow fast algorithms. Complementing the results on simplicity for polyhedral norms, we prove that for the case of Euclidean distances in R^d for d>2, the Maximum TSP is NP-hard. This sheds new light on the well-studied difficulties of Euclidean distances.Comment: 24 pages, 6 figures; revised to appear in Journal of the ACM. (clarified some minor points, fixed typos

Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution

In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of medical image transformation problems, such as super-resolution (SR) and image synthesis. However, the highly ill-posed nature of such problems results in inevitable ambiguity in the learning of networks. We propose to account for intrinsic uncertainty through a per-patch heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference in the form of variational dropout. We show that the combined benefits of both lead to the state-of-the-art performance SR of diffusion MR brain images in terms of errors compared to ground truth. We further show that the reduced error scores produce tangible benefits in downstream tractography. In addition, the probabilistic nature of the methods naturally confers a mechanism to quantify uncertainty over the super-resolved output. We demonstrate through experiments on both healthy and pathological brains the potential utility of such an uncertainty measure in the risk assessment of the super-resolved images for subsequent clinical use.Comment: Accepted paper at MICCAI 201

Novel Scientific Evidence of Intoxication: Acoustic Analysis of Voice Recordings from the Exxon Valdez

Part of this article reports original research conducted under the direction of the second and third authors. The initial re search was supported by a contract to Indiana University from General Motors Research Laboratories. The specific analyses of voice recordings of Captain Joseph Hazelwood were conducted by them at the re quest of the National Transportation Safety Board, and are based on tapes and data supplied by the NTSB. The second author may be called as a witness in some of the lawsuits pending against the Exxon Corporation. The opinions expressed in this article concerning whether this evidence meets the legal standards of reliability and admissibility are those of the first author, who is not affiliated with the Speech Research Laboratory and has not participated in either the initial research nor the analysis of the Exxon Valdez tapes

Trends in Correctional Control by Race and Sex

American prison populations have long been characterized by racial and ethnic disparities. U.S. Census Bureau data on incarcerated persons from 1870 through 1980 show that black incarceration rates ranged from three to nine times those of whites, depending upon the decade and region of the country.In recent years, racial disparities in imprisonment have decreased. This Council on Criminal Justice report updates and advances earlier presentations of data on disparities